aif360.algorithms.inprocessing
.PrejudiceRemover¶
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class
aif360.algorithms.inprocessing.
PrejudiceRemover
(eta=1.0, sensitive_attr='', class_attr='')[source]¶ Prejudice remover is an in-processing technique that adds a discrimination-aware regularization term to the learning objective [6].
References
[6] T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma, “Fairness-Aware Classifier with Prejudice Remover Regularizer,” Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2012. Parameters: Methods
fit
Learns the regularized logistic regression model. fit_predict
Train a model on the input and predict the labels. fit_transform
Train a model on the input and transform the dataset accordingly. predict
Obtain the predictions for the provided dataset using the learned prejudice remover model. transform
Return a new dataset generated by running this Transformer on the input. -
fit
(dataset)[source]¶ Learns the regularized logistic regression model.
Parameters: dataset (BinaryLabelDataset) – Dataset containing true labels. Returns: PrejudiceRemover – Returns self.
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predict
(dataset)[source]¶ Obtain the predictions for the provided dataset using the learned prejudice remover model.
Parameters: dataset (BinaryLabelDataset) – Dataset containing labels that needs to be transformed. Returns: dataset (BinaryLabelDataset) – Transformed dataset.
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